auto-arabic-summarization
| Property | Value |
|---|---|
| Framework | PyTorch 1.13.0+cu116 |
| Training Metrics | Rouge-L: 1.137, Loss: 0.829 |
| Environmental Impact | 23.93g CO2 emissions |
| Author | abdalrahmanshahrour |
What is auto-arabic-summarization?
auto-arabic-summarization is a specialized Arabic text summarization model built using the BERT2BERT architecture and trained on Modern Standard Arabic (MSA) content. The model leverages advanced transformer technology to generate concise summaries and news titles from Arabic text, while also supporting paraphrasing capabilities.
Implementation Details
The model is implemented using the Transformers library (v4.25.1) and PyTorch framework. It utilizes the mBART architecture and incorporates elements from AraBERT, demonstrating strong performance in text-to-text generation tasks. The model achieved a validation loss of 0.829 and competitive ROUGE scores during evaluation.
- Built on BERT2BERT architecture optimized for Arabic language
- Trained using AutoTrain with environment-conscious monitoring
- Supports multiple text generation tasks including summarization and title generation
Core Capabilities
- Arabic text summarization with focus on MSA content
- News headline generation from article content
- Arabic text paraphrasing
- Optimized for modern Arabic language processing
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on Arabic language processing, combining BERT2BERT architecture with Modern Standard Arabic optimization. Its versatility in handling multiple text generation tasks while maintaining relatively low environmental impact (23.93g CO2) makes it particularly valuable for Arabic NLP applications.
Q: What are the recommended use cases?
The model is best suited for Arabic content processing tasks including: news article summarization, automated headline generation, content paraphrasing for Arabic texts, and general text condensation for Modern Standard Arabic content. It's particularly valuable for media organizations and content producers working with Arabic language materials.
